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IT COULD HAPPEN THE MAJOR LEAGUES IN 2012 A WIN In spring, baseball hopes spring eternal even though the odds are ACCOUNTING long for most teams. But sometimes the dreams come through. ANALYSIS What does it take to manufacture a miracle season? How realistic are these hopes for the 2012 major league teams? This book evaluates these hopes quantitatively using Baseball Win Accounting. Baseball Win Accounting provides an analytical framework for baseball using the on-base-plus-slugging (OPS) measure of performance for team offense and defense as the unit of account. It shows how hitting, pitching and even fielding can be evaluated using this consistent measure of performance, and how to aggregate player performance to team wins. IT COULD HAPPEN

THE MAJOR LEAGUES IN 2012

A BASEBALL WIN ACCOUNTING ANALYSIS

Dan Ciuriak April 2012

Copyright Dan Ciuriak, 2012. All Rights Reserved

Foreword

In baseball, hope springs eternal. Every year yields a new lineup for each team. The churn includes rookies, free agents and players moving into and out of the lineup, coming off injuries and so forth. It’s possible to dream and sometimes the dreams come through. The perennial problem is how to add up the impact of all the changes?

This book tackles the adding up problem quantitatively using Baseball Win Accounting.

Baseball generates a lot of raw statistics and baseball analysts have developed many more. Baseball Win Accounting doesn’t generate any new stats but rather it provides an innovative way to allow us to use powerful existing statistics, ones with which we’re already familiar, to better advantage.

Baseball is a zero sum game: what a hitter achieves is at a ’s expense. Evaluating a hitter in terms of one statistic (e.g., runs created, offensive or the old garden variety crown stats) and the pitcher by another (ERA, WHIP or something more exotic) makes it impossible to draw direct comparisons.

Moreover, many of these stats describe individual player performances in terms of team results, such as winning percentage. But players don’t win, teams do. Nor do players create runs – runs are the outcomes of sequences of events – they are team results. So to calculate an individual’s winning percentage or runs created is to create a confusion.

To deal with the adding up problem in a consistent way, the contributions of hitters, and fielders have to be expressed in a common statistic that is both a meaningful measure of individual performance and also, at the team level, closely correlated with team success. This can be done by applying analytically the statistical information already at our disposal – this is what the Baseball Win Accounting framework does.

The key to any accounting framework is to use a common basis for evaluation. Of the readily available statistics that are the same for both pitchers and hitters, by far the most meaningful is the sum of the on-base percentage and slugging average (OPS) for hitters and the corresponding on-base plus against (OPSA) for pitchers.

Baseball Win Accounting develops a framework for applying these statistics in a consistent manner to evaluate team performance, both retrospectively and prospectively. The box below summarizes the elements; the rest of this book develops the underpinning for each of the relationships. The full framework is developed and discussed in Part II of this book. This is from the original 2001 edition of this book; an updated version is planned for next year’s book.

1. Team OPS = the average of the individual players’ OPSs, weighted by plate appearances. 2. Team OPSA = the average of the individual pitchers’ OPSAs, weighted by pitched. 3. One additional point on a team’s OPS translates into two additional runs scored over the course of a 162 game season. 4. One additional point on a team’s OPSA translates into two additional runs scored against over the course of a 162 game season. 5. When baseball is in a zone where games average nine runs per game, adding nine additional runs to a team’s margin of runs for and against over the course of a season adds on average one more win to the team’s total.

Using this approach, this book dreams about the upcoming season in numbers. How realistic are hopes for the 2012 major league teams? How for real are the favourites? How long are the odds on the long shots? Is there any basis for nurturing a hope that a perennial cellar dweller might break out and manufacture a miracle season?

Part I of this book looks at each of the major league teams and considers what it would take for the team to have a shot at the postseason—working within realistic performance bounds for individual players.

A word about the projection method. The underlying methodology, Baseball Win Accounting, is developed in Part II of this book. The projections in Part I aren’t predictions, they are more in the nature of assessments of potential. Based on projected line-ups, assigned roles, depth charts and proven ability to deserve plate appearances and , each player is given a guesstimated number of plate appears (PA) and each pitcher an innings pitched (IP) total. Their performance level is measured in terms of on-base plus slugging percentage (OPS) for hitter sand in terms of on-base plus slugging percentage against (OPSA) for pitchers. The beauty of this approach is that it is a zero sum game. Whatever hitters achieve is exactly equal to what the pitchers give up. Each team’s overall OPS and OPSA are equal to the average of the players/pitchers scores, weighted by their PAs and IPs. Calibrated to the current environment, a team is projected to record runs for and runs against totals in line with their respective OPSs and OPSAs. In turn, the margin of runs for less runs against, divided by 9 added to 81 gives a team’s total projected wins.

For player forecasts, I allow the numbers to speak: each player is given the average of the past five seasons, subject to adjustments if: (a) there has been an apparent distinct change in level of performance, (b) if there is a clear trend in the level of performance, (c) if the average is distorted by an exceptionally good or bad season out of line with the player’s overall minor and major league history, and so forth. For players with limited PAs or IPs in the majors, I take the relevant minor league average and discount it for change in level of play. I look at fantasy baseball projections and these appear to be based on the same principles. Because the results of the preceding season influence the projected lineups for the coming sense, there is a tendency for the projected lineups across all 30 major leagues to have a winning percentage greater than .500 and runs for to exceed runs against. Since the overall winning percentage for the league must be .500, and runs for must equal runs against, an adjustment is required. This is necessarily arbitrary. I bring the accounts into line by raising the OPSAs of pitchers by an amount needed to bring runs against into line with runs for. The adjustments are relatively small for any individual pitcher and do not affect the order of finish of the teams.

The approach described above is obviously strongly influenced by mean reversion—that is, teams perform according to average levels of performance. By the same token, overall team outcomes tend to get bunched more towards .500 than is typical in a major league season. This is unavoidable and actually quite realistic as an expectation at the start of a season. Over the course of the season, the year-to-year variance in player performance takes over and shapes outcomes. Some players do better than average and some do worse, but the plus/minuses are not evenly balanced by team. Injuries are suffered and other shit happens. Some players have breakout seasons (sometimes apparently with a little help from the bottle). Some teams get lucky and others win fewer games than they appear to deserve on paper. At some point, some teams give up on the season and sell-off expensive players; other teams with a shot at the playoffs meanwhile do the opposite and spend money to lift their performance. All of these factors tend to create greater dispersion in the final results than are implied by the season-opening strategies

Once the baseline projection is established, it is possible to ask the question of how much head room any particular team has. I approach this question in the following way. For an established player with a consistent record, there is a certain amount of variation in annual OPS and OPSA results. For example, of the LA Dodgers has had the following OPS results and number of plate appearances over the last five seasons:

Matt Kemp Team Position OPS PA 2007 Kemp, M LAD CF 0.894 311 2008 Kemp, M LAD CF 0.799 657 2009 Kemp, M LAD CF 0.842 667 2010 Kemp, M LAD CF 0.760 668 2011 Kemp, M LAD CF 0.986 689 Average 0.853 598 StDev .088

One way to express the amount of annual variation in the OPS is to calculate the standard deviation of the five OPS scores (StDev). This assumes that the OPS scores are generated by a random process which yields a given mean (the average OPS core of .853) and certain amount of variation around that mean that is characteristic of that process (in this case, the batter’s ability). Strictly speaking, it is not possible to interpret the variation in Matt Kemp’s OPS scores in this manner since each season is generated by a Matt Kemp of different age, experience, health and weather conditions, not to mention different pitchers faced. However, as a way to describe the observed variation it is not entirely unreasonable given the high random element in baseball (see Chapter 8 of Part II for a discussion) and, even though the mean performance is trending, it is reasonably representative of the batter’s ability then and now. Another handy feature of standard deviation is that it provides a measure of confidence that we have in the next trial falling within a given distance from the mean. About 2/3 of the time, we would expect another value from this process (Matt Kemp through a season) to be in the range of .853 +/- .088, in other words between .765 and .941. The 1.000 season that Kemp is on track to deliver in 2012 is thus an outlier based on his established performance. In building up reasonable cases for “miracle” seasons, I use the mean plus one StDev as a reasonable measure of upside for that player; for a pitcher it’s the OPSA minus one StDev.

On that basis, below is the blow by blow for the 2012 season. Enjoy. Feedback welcome.

Dan Ciuriak Ottawa 29 April 2012

PART I

IT COULD HAPPEN – AND HERE’S HOW

THE MAJOR LEAGUES IN 2012

National League East

In the NL East, the have posted phenomenal pitching numbers, implying a 200+ run differential if sustained for a whole season and over 100 wins. That probably happen but it demonstrates how well backed-up the won-loss record is with on-field pitching and hitting. But we don’t have to look hard to see which team is likely to have the miracle this year.

The Phillies, conversely, who have dominated this division in recent years, are off to a bad start—not unexpected given the absence of and —but don’t have the horses in any event. The 2011 season was a bit of a mirage as the Phils won 8 games more than their pitching/hitting performance warranted. By the same token, the fall-off this year is not really as far as the won-loss column suggests.

OPS OPSAG Runs Runs Ag Margin Games Wins Losses Win Pct Braves 0.734 0.702 723 659 64 162 88 74 0.543 0.752 0.731 760 717 43 162 86 76 0.531 Washington Nationals 0.713 0.700 681 655 26 162 84 78 0.519 0.732 0.732 719 719 0 162 81 81 0.500 0.713 0.717 682 690 -8 162 80 82 0.494

Atlanta Braves

The Braves converted OPS and OPSA into runs and runs against consistent with form but were less consistent in converting run margin into wins—until 2011 (a one-run win effect – not a Kimbrel effect). The only noteworthy bump in the road for the Braves has been the early season exit of Jair Jurrjens who has not recovered from his second half 2011 injury. No worries, the Braves are deep in arms.

W L OPS OPSA R RAG Margin R* RAG* Margin* W* W** 2007 84 78 0.774 0.742 810 733 77 809 745 64 88 89 2008 72 90 0.753 0.758 753 778 -25 761 771 -10 79 78 2009 86 76 0.744 0.713 735 641 94 734 672 62 87 91 2010 91 71 0.740 0.690 738 629 109 734 634 100 91 92 2011 89 73 0.695 0.671 641 605 36 646 598 48 86 84 2012 88 74 0.734 0.702 723 659 64 88 Note: * indicates a value predicted by BWA. ** is the win total predicted by the actual run margin.

Player Age OPS - Ave StDev OPS* Adj* PA Comment C Brian McCann 28 0.830 0.044 0.830 0.000 554 In form 1B 23 0.784 0.209 0.784 0.000 550 In form 2B 32 0.826 0.048 0.826 0.000 672 In form SS Tyler Pastornicky 23 0.000 NA 0.619 0.000 500 Minors ave factored in 3B 40 0.910 0.123 0.814 -0.096 475 Level change LF Martin Prado 28 0.776 0.082 0.776 0.000 575 In form CF 30 0.695 0.063 0.695 0.000 600 In form RF 23 0.789 0.100 0.789 0.000 540 In form Bench 0.676 1308 Pitchers 0.360 360 Total 0.725 6134

Pitcher Age OPSA - Ave StDev OPSA* Adj* IP Comment SP 37 0.652 0.056 0.658 0.006 180 In form SP Tommy Hansen 26 0.660 0.016 0.667 0.006 200 In form SP Mike Minor 25 0.816 0.067 0.782 -0.035 180 Minors ave factored in SP Brandon Beachy 26 0.679 0.001 0.701 0.022 180 Minors ave factored in SP Randall Delgado 22 0.655 NA 0.787 0.132 140 Minors ave factored in CL 24 0.486 NA 0.493 0.007 70 In form 0.709 508 Team 0.702 1458

Can the Braves withstand a surge by the Nationals?

The NL East is so close on paper that the Braves may have to turn in a plus performance to win the division, especially in view of the Nationals’ strong start. How big is their upside? Plenty. Several of the Braves position players have established levels of performance that permit realistic hopes of a premium yield over their average levels. The Braves could meet a Nationals’ challenge and still prevail, even with the pitching performance basically in form. This is what it would look like (note: this is the Nationals’ “miracle” year combined with a Braves’ surge and the overall NL league outcomes adjusted to keep runs = runs against and wins totals equal).

2012 OPS OPSA Runs Runs Ag Margin Games Wins Losses Win Pct NL East 0.772 0.702 800 659 141 162 97 65 0.599 NL East Washington Nationals 0.747 0.690 748 635 113 162 94 68 0.580 NL East Miami Marlins 0.752 0.737 760 730 30 162 84 78 0.519 NL East New York Mets 0.732 0.738 719 732 -13 162 80 82 0.494 NL East Philadelphia Phillies 0.713 0.724 682 702 -20 162 79 83 0.488

And here’s how it would be done on the field:

Position Player Age OPS - Ave StDev OPS* Adj^ PA Comment C Brian McCann 28 0.830 0.044 0.874 0.044 554 One StDev plus 1B Freddie Freeman 23 0.784 0.209 0.784 0.000 600 In form 2B Dan Uggla 32 0.826 0.048 0.874 0.048 672 One StDev plus SS Tyler Pastornicky 23 0.000 NA 0.619 0.000 500 Minors ave factored in 3B Chipper Jones 40 0.910 0.123 0.910 0.000 475 Average performance LF Martin Prado 28 0.776 0.082 0.838 0.062 575 One StDev plus CF Michael Bourn 30 0.695 0.063 0.759 0.063 600 One StDev plus RF Jason Heyward 23 0.789 0.100 0.889 0.100 600 One StDev plus Bench 0.714 1208 Pitchers 0.360 360 Total 0.772 6144

Position Player Age OPSA - Ave StDev OPSA* Adj* IP Comment SP Tim Hudson 37 0.652 0.056 0.658 0.006 180 In form SP Tommy Hansen 26 0.660 0.016 0.667 0.006 200 In form SP Mike Minor 25 0.816 0.067 0.782 -0.035 180 Minors ave factored in SP Brandon Beachy 26 0.679 0.001 0.701 0.022 180 Minors ave factored in SP Randall Delgado 22 0.655 #DIV/0! 0.787 0.132 140 Minors ave factored in CL Craig Kimbrel 24 0.486 NA 0.493 0.007 70 In form Bullpen 0.709 508 Team 0.702 1458

Miami Marlins

On paper, the Marlins should have averaged about 81 wins over the 2007-2011 seasons; in reality they managed only 79 wins on average. They were not as efficient as the average team in translating OPS-OPSA into runs-runs against but a bit more efficient than average in converting run margins into seasons win totals. The 2012 squad adds Jose Reyes at , coming over from the Mets, and and Carlos Zambrano to the rotation coming over the Chisox and Cubs. Add it all up in BWA terms and it’s a much better team than in 2011 and a legitimate challenger in the NL East, even with a downgrade to staff ace coming off shoulder surgery.

W L OPS OPSA R RAG Margin R* RAG* Margin* W* W** 2007 71 91 0.784 0.806 790 891 -101 829 873 -44 76 69 2008 84 77 0.759 0.740 770 767 3 779 741 38 85 81 2009 87 75 0.756 0.741 772 766 6 767 737 30 84 81 2010 80 82 0.724 0.728 719 717 2 694 702 -8 80 81 2011 72 90 0.706 0.718 625 702 -77 665 690 -25 78 72 2012 86 76 0.752 0.731 760 717 43 86 Note: * indicates a value predicted by BWA. ** is the win total predicted by the actual run margin.

Player Age OPS - Ave StDev OPS* Adj* PA Comment C 32 0.727 0.059 0.727 0.000 450 In form 1B Gaby Sanchez 29 0.786 0.103 0.786 0.000 650 In form 2B 31 0.731 0.047 0.731 0.000 550 In form SS Jose Reyes 29 0.804 0.056 0.804 0.000 590 In form 3B Hanley Ramirez 29 0.898 0.103 0.898 0.000 611 In form LF Logan Morrison 25 0.811 0.028 0.811 0.000 550 In form CF 27 0.676 0.110 0.676 0.000 550 In form RF 23 0.869 0.042 0.869 0.000 600 In form Bench 0.723 1275 Pitchers 0.360 360 Total 0.752 6186

Pitcher Age OPSA - Ave StDev OPSA* Adj* IP Comment SP Josh Johnson 28 0.633 0.180 0.820 0.187 180 Shoulder surgery in 2011 SP Mark Buehrle 32 0.742 0.013 0.749 0.006 210 In form SP 30 0.741 0.062 0.747 0.006 195 In form SP Carlos Zambrano 31 0.706 0.046 0.712 0.006 170 In form SP Anibal Sanchez 28 0.726 0.097 0.732 0.006 195 In form CL 35 0.573 0.049 0.580 0.006 60 In form Bullpen 0.707 448 Team 0.731 1458

Can the Marlins take the division?

The NL East is close on paper and the Marlins could easily win on luck alone. However, they also have a bit of upside that could relatively easily push them over the top. Players with plausible head-room are John Buck and Jose Reyes, who have gotten off to slow start, and Omar Infante, who has gotten off to a hot start with 4 home runs in the young season. Add in a bit more optimistic outcome for Josh Johnson and a bigger season from Carlos Zambrano (an assumption that looks good based on the early season) and the Marlins have the horses to finish first. Whether they have what it takes to surmount a Nationals or Braves miracle season is less clear. Their minor league talent mirrors rather than complements their major league strengths.

2012 OPS OPSA Runs Runs Ag Margin Games Wins Losses Win Pct NL East Miami Marlins 0.776 0.722 807 699 108 162 93 69 0.574 NL East Atlanta Braves 0.734 0.704 723 664 59 162 88 74 0.543 NL East Washington Nationals 0.713 0.703 681 660 21 162 83 79 0.512 NL East New York Mets 0.732 0.735 719 724 -5 162 80 82 0.494 NL East Philadelphia Phillies 0.713 0.720 682 694 -12 162 80 82 0.494

And here’s how it could be done on the field:

Position Player Age OPS - Ave StDev OPS* Adj^ PA Comment C John Buck 32 0.727 0.059 0.786 0.059 450 One StDev plus 1B Gaby Sanchez 29 0.786 0.103 0.786 0.000 650 In form 2B Omar Infante 31 0.731 0.047 0.778 0.047 550 One StDev plus SS Jose Reyes 29 0.804 0.056 0.861 0.056 590 One StDev plus 3B Hanley Ramirez 29 0.898 0.103 0.898 0.000 611 In form LF Logan Morrison 25 0.811 0.028 0.811 0.000 550 In form CF Emilio Bonifacio 27 0.676 0.110 0.787 0.110 550 One StDev plus RF Giancarlo Stanton 23 0.869 0.042 0.869 0.000 600 In form Bench 0.723 1275 Pitchers 0.360 360 Total 0.776 6186

Position Player Age OPSA - Ave StDev OPSA* Adj* IP Comment SP Josh Johnson 28 0.633 0.180 0.773 0.140 180 Early 2012 OPSA sustained SP Mark Buehrle 32 0.742 0.013 0.751 0.009 210 In form SP Ricky Nolasco 30 0.741 0.062 0.750 0.009 195 In form SP Carlos Zambrano 31 0.706 0.046 0.669 -0.037 170 One StDev lower SP Anibal Sanchez 28 0.726 0.097 0.735 0.009 195 In form CL Heath Bell 35 0.573 0.049 0.582 0.009 60 In form Bullpen 0.710 448 Team 0.722 1458 Washington Nationals

The Nationals forged a 68-win average over the 2007-2011 seasons compared to a predicted 70- win average. They converted OPS/OPSA into runs and runs against almost bang on the formula; the problem lay in converting run margin into wins. That usually evens out over time. What’s changed for 2012 is the rotation. The addition of and Gio Gonzalez, with Jordan Zimmerman, gives the Nationals a formidable front end of the rotation. The rest we’ll see about. The features two established stars in and , both coming off sub-par years, and then a bunch of journeymen. So, not a 100+-win team— unless maybe it is this year’s miracle.

W L OPS OPSA R RAG Margin R* RAG* Margin* W* W** 2007 73 89 0.715 0.772 673 783 -110 691 805 -114 68 68 2008 59 102 0.696 0.778 641 825 -184 647 811 -164 63 60 2009 59 103 0.743 0.802 710 874 -164 732 850 -118 68 63 2010 69 93 0.708 0.740 655 742 -87 670 734 -64 74 71 2011 80 81 0.691 0.701 624 643 -19 638 658 -20 78 78 2012 84 78 0.713 0.700 681 655 26 84 Note: * indicates a value predicted by BWA. ** is the win total predicted by the actual run margin.

Player Age OPS - Ave StDev OPS* Adj* PA Comment C 25 0.768 0.040 0.688 -0.080 450 minors ave factored in 1B Adam LaRoche 32 0.800 0.123 0.800 0.000 521 In form 2B Danny Espinosa 25 0.735 0.010 0.735 0.000 650 In form SS 27 0.691 0.118 0.691 0.000 651 In form 3B Ryan Zimmerman 28 0.834 0.059 0.834 0.000 585 In form LF 30 0.890 0.137 0.772 -0.118 350 Injury discount CF Rick Ankiel 33 0.741 0.097 0.675 -0.066 408 In form RF Jayson Werth 32 0.846 0.077 0.846 0.000 553 In form Bench 0.676 1550 Pitchers 0.360 360 Total 0.713 6078

Pitcher Age OPSA - Ave StDev OPSA* Adj* IP Comment SP Stephen Strasburg 24 0.544 0.140 0.551 0.006 204 In form SP Gio Gonzalez 27 0.702 0.135 0.657 -0.045 200 Change in level SP 26 0.716 0.074 0.722 0.006 160 In form SP 29 0.768 0.061 0.775 0.006 193 In form SP 26 0.748 0.386 0.755 0.006 160 In form CL Henry Rodriguez 37 0.615 0.043 0.621 0.006 60 In form 0.736 481 0.700 1458

How big a miracle could the Nationals ultimately forge in 2012?

Stephen Strasburg’s .554 OPSA sustained for a full season is already among the all-time great seasons and Gio Gonzalez is already pencilled in at the best he’s done so no head room there either. Jordan Zimmerman and Edwin Jackson could match their best seasons and both are off to good starts. Jayson Werth, Ryan Zimmerman and Rick Ankiel rebounding from off-years to match their best ever puts the Nationals in first place, in Zimmerman’s case overcoming early season shoulder problems. In the minors, interesting cases are (CF) playing AAA ball at 19, (3B), the leading college hitter in last year’s draft, Tyler Moore (1B) putting up big power numbers for the third year in a row, and 6’9” who is putting up stuff-of-dreams K totals in the lower minors to add depth to the dream.

2012 OPS OPSA Runs Runs Ag Margin Games Wins Losses Win Pct NL East Washington Nationals 0.747 0.690 748 635 113 162 94 68 0.580 NL East Atlanta Braves 0.734 0.705 723 665 58 162 87 75 0.537 NL East Miami Marlins 0.752 0.734 760 723 37 162 85 77 0.525 NL East New York Mets 0.732 0.735 719 725 -6 162 80 82 0.494 NL East Philadelphia Phillies 0.713 0.720 682 696 -14 162 79 83 0.488

And here’s how it would be done on the field (minor league phenoms not included):

Position Player Age OPS - Ave StDev OPS* Adj^ PA Comment C Wilson Ramos 25 0.768 0.040 0.768 0.000 450 minors ave factored in 1B Adam LaRoche 32 0.800 0.123 0.800 0.000 550 In form 2B Danny Espinosa 25 0.735 0.010 0.735 0.000 650 In form SS Ian Desmond 27 0.691 0.118 0.691 0.000 651 In form 3B Ryan Zimmerman 28 0.834 0.059 0.893 0.059 585 One StDev plus LF Michael Morse 30 0.890 0.137 0.772 -0.118 350 Injury discount CF Rick Ankiel 33 0.741 0.097 0.838 0.097 550 One StDev plus RF Jayson Werth 32 0.846 0.077 0.923 0.077 553 One StDev plus Bench 0.677 1400 Pitchers 0.360 360 Total 0.747 6099

Position Player Age OPSA - Ave StDev OPSA* Adj* IP Comment SP Stephen Strasburg 24 0.544 0.140 0.554 0.009 204 In form SP Gio Gonzalez 27 0.702 0.135 0.659 -0.042 200 Change in level SP Jordan Zimmermann 26 0.716 0.074 0.680 -0.035 160 One StDev lower SP Edwin Jackson 29 0.768 0.061 0.717 -0.051 193 One StDev lower SP Ross Detwiler 26 0.748 0.386 0.758 0.009 160 In form CL Henry Rodriguez 37 0.615 0.043 0.624 0.009 60 In form Bullpen 0.739 481 Team 0.690 1458 New York Mets

The Mets scored almost exactly the total number of runs predicted by their OPS over the 2007- 2011 combined and gave only a few runs more. In 2011, they were full value for their 77 wins. Looking forward in 2012, the Mets have to regroup after losing Jose Reyes and Carlos Beltran. The early season injury to Jason Bay adds another hole to the batting lineup. A strong rebound by from an off-season and some help from the minors in the form of have helped offset these losses out of the gate but it is unlikely that Nieuwenhuis can sustain that – his early season major league OPS is well above his minor league numbers. Adding things up, the Mets look like a .500 team in 2012, a surprise given their losses.

W L OPS OPSA R RAG Margin R* RAG* Margin* W* W** 2007 88 74 0.775 0.737 804 750 54 811 735 76 89 86 2008 89 73 0.761 0.729 799 715 84 777 713 64 87 90 2009 70 92 0.729 0.759 671 757 -86 704 764 -60 74 71 2010 79 83 0.697 0.723 656 652 4 648 700 -52 75 81 2011 77 85 0.725 0.743 718 742 -24 706 742 -36 77 78 2012 81 81 0.732 0.732 719 719 0 81 Note: * indicates a value predicted by BWA. ** is the win total predicted by the actual run margin.

Player Age OPS - Ave StDev OPS* Adj* PA Comment C 26 0.707 0.031 0.707 0.000 450 In form 1B 25 0.818 0.095 0.818 0.000 550 In form 2B Daniel Murphy 27 0.784 0.065 0.784 0.000 650 In form SS Ruben Tejada 23 0.652 0.076 0.652 0.000 550 In form 3B David Wright 30 0.880 0.075 0.880 0.000 636 In form LF Jason Bay 34 0.813 0.096 0.813 0.000 300 In form CF Kirk Nieuwenhuis 25 0.719 0.000 0.719 0.000 400 Minors ave discounted RF 26 0.816 0.123 0.816 0.000 550 In form Bench 0.700 1600 Pitchers 0.360 360 Total 0.732 6046

Pitcher Age OPSA - Ave StDev OPSA* Adj* IP Comment SP 32 0.665 0.022 0.694 0.029 195 Missed 2011/injury discount SP R.A. Dickey 38 0.719 0.082 0.726 0.006 180 In form SP 26 0.773 0.097 0.780 0.006 165 In form SP 28 0.761 0.036 0.767 0.006 195 In form SP 26 0.721 0.076 0.727 0.006 160 In form CL Frank Francisco 32 0.686 0.052 0.692 0.006 55 In form Bullpen 0.726 508 Team 0.732 1458

Can the Mets manufacture a miracle?

The Mets have little upside offensively. Realistically, the middle , Ruben Tejada and Daniel Murphy, could match their 2011 seasons and Josh Thole might hold onto some of his hot start for an above-average year. Most importantly Ike Davis and Lucas Duda need to maintain their average form and provide a lot of quality at bats. Pitching is where the Mets have to pin their hopes. Getting a high-end season from Johan Santana is essential, and Jon Niese and Mike Pelfrey have upside potential compared to their averages. But that’s far from enough. A concerted good year from the bullpen is needed. On top of that, the Mets probably need a surprise from the bench and/or help from the farm (e.g., a big year from Zack Wheeler or perhaps to plug any leaks that the staff develops over the course of the season. Then you get your miracle. Here’s what it would look like (without the phenoms factored in):

2012 OPS OPSA Runs Runs Ag Margin Games Wins Losses Win Pct NL East New York Mets 0.754 0.711 764 678 86 162 91 71 0.562 NL East Atlanta Braves 0.734 0.705 723 665 58 162 87 75 0.537 NL East Miami Marlins 0.752 0.734 760 723 37 162 85 77 0.525 NL East Washington Nationals 0.713 0.703 681 661 20 162 83 79 0.512 NL East Philadelphia Phillies 0.713 0.720 682 696 -14 162 79 83 0.488 And here’s how it could be done on the field: Position Player Age OPS - Ave StDev OPS* Adj^ PA Comment C Josh Thole 26 0.707 0.031 0.738 0.031 550 One StDev higher 1B Ike Davis 25 0.818 0.095 0.818 0.000 650 In form 2B Daniel Murphy 27 0.784 0.065 0.809 0.025 650 2011 repeat SS Ruben Tejada 23 0.652 0.076 0.696 0.044 550 2011 repeat 3B David Wright 30 0.880 0.075 0.880 0.000 636 In form LF Jason Bay 34 0.813 0.096 0.813 0.000 300 In form CF Kirk Nieuwenhuis 25 0.719 0.000 0.719 0.000 400 Minors ave discounted RF Lucas Duda 26 0.816 0.123 0.816 0.000 550 In form Bench 0.748 1450 Pitchers 0.360 360 Total 0.754 6096

Position Player Age OPSA - Ave StDev OPSA* Adj* IP Comment SP Johan Santana 32 0.665 0.022 0.653 -0.012 234 One StDev lower SP R.A. Dickey 38 0.719 0.082 0.729 0.010 180 In form SP Jon Niese 26 0.773 0.097 0.764 -0.009 175 Matches 2011 SP Mike Pelfrey 28 0.761 0.036 0.735 -0.026 200 One StDev lower SP Dillon Gee 26 0.721 0.076 0.730 0.010 160 In form CL Frank Francisco 32 0.686 0.052 0.696 0.010 55 In form Bullpen 0.702 634 Concerted good years Team 0.711 1638 Philadelphia Phillies

The Phillies beat the system the past five years. Their runs scored was generally quite consistent with the team OPS, but consistently just a smidgen higher. The runs against were generally consistent with the OPS but a somewhat larger smidgen lower. The result was an unusual cumulation of “errors”: the Phils had close to a 300 run cumulative margin over the 2007-2011 seasons than their OPS-OPSA warranted. That said, they win totals were very close to those predicted by their actual margins. One major insight from this is that the 2011 team was not close to full value for the 102 wins. By the same token, unless the Phils can continue to pull of this trick, the fall-off in 2012 will be much greater, especially as they face a first half without Ryan Howard and Chase Utley.

W L OPS OPSA R RAG Margin R* RAG* Margin* W* W** 2007 89 73 0.812 0.797 892 821 71 885 855 30 84 88 2008 92 70 0.770 0.739 799 680 119 795 733 62 87 93 2009 93 69 0.781 0.757 820 709 111 808 760 48 86 92 2010 97 65 0.745 0.716 772 640 132 744 686 58 87 95 2011 102 60 0.717 0.657 713 529 184 690 570 120 94 101 2012 80 82 0.713 0.717 682 690 -8 80 Note: * indicates a value predicted by BWA. ** is the win total predicted by the actual run margin.

Player Age OPS - Ave StDev OPS* Adj* PA Comment C 33 0.750 0.083 0.750 0.000 417 In form 1B Ryan Howard 32 0.897 0.057 0.840 -0.057 300 Injury discount 2B Charles Utley 34 0.889 0.080 0.809 -0.080 300 Injury discount SS 34 0.771 0.072 0.736 -0.035 631 2007 outlier 3B Placido Polanco 37 0.751 0.064 0.687 -0.064 550 Declining trend LF John Mayberry 29 0.846 0.256 0.704 -0.142 550 Minors ave factored in CF 32 0.795 0.035 0.795 0.000 613 In form RF 29 0.828 0.000 0.828 0.000 620 In form Bench 0.672 1718 Pitchers 0.360 360 Total 0.713 6059

Pitcher Age OPSA - Ave StDev OPSA* Adj* IP Comment SP 35 0.638 0.038 0.645 0.006 239 In form SP 34 0.659 0.087 0.665 0.006 180 In form SP 29 0.675 0.058 0.682 0.006 206 In form SP 25 0.658 0.114 0.777 0.118 160 Minors ave factored in SP 32 0.748 0.053 0.782 0.033 168 2007 outlier CL 32 0.572 0.000 0.617 0.045 65 In form 0.763 441 0.717 1458 Can the Phillies fashion a miracle?

The Phillies have enough legitimate upside in the rotation that a Division title cannot be ruled out. With a few better than average years from Carlos Ruiz, Shane Victorino and Hunter Pence, and the rest of the lineup conspiring to deliver average performances, the Phillies would have enough offence to back a pitching staff on which Halladay, Lee and Hamels are at the top of their game. Can the Phillies overcome the Nationals’ strong start? Not plausibly without some help from the minors. The Phils’ hope here is who is turning in strong numbers in AA ball to start the season. An early move up and a big break-in season would put the Phillies over the top.

2012 OPS OPSA Runs Runs Ag Margin Games Wins Losses Win Pct NL East Philadelphia Phillies 0.739 0.688 733 631 102 162 92 70 0.568 NL East Atlanta Braves 0.734 0.705 723 665 58 162 87 75 0.537 NL East Miami Marlins 0.752 0.735 760 724 36 162 85 77 0.525 NL East Washington Nationals 0.713 0.704 681 662 19 162 83 79 0.512 NL East New York Mets 0.732 0.736 719 726 -7 162 80 82 0.494 And here’s how it could be done on the field: Position Player Age OPS - Ave StDev OPS* Adj^ PA Comment C Carlos Ruiz 33 0.750 0.083 0.833 0.083 417 One StDev higher 1B Ryan Howard 32 0.897 0.057 0.840 -0.057 300 Injury discount 2B Charles Utley 34 0.889 0.080 0.809 -0.080 300 Injury discount SS Jimmy Rollins 34 0.771 0.072 0.736 -0.035 631 2007 outlier 3B Placido Polanco 37 0.751 0.064 0.687 -0.064 550 Declining trend LF John Mayberry 29 0.846 0.256 0.704 -0.142 550 Minors ave factored in CF Shane Victorino 32 0.795 0.035 0.830 0.035 613 One StDev higher RF Hunter Pence 29 0.828 0.047 0.875 0.047 620 One StDev higher Bench 0.713 1718 Pitchers 0.360 360 Total 0.739 6059

Position Player Age OPSA - Ave StDev OPSA* Adj* IP Comment SP Roy Halladay 35 0.638 0.038 0.610 -0.028 239 One StDeve lower SP Cliff Lee 34 0.659 0.087 0.618 -0.041 180 2011 repeat SP Cole Hamels 29 0.675 0.058 0.628 -0.047 206 One StDeve lower SP Vance Worley 25 0.658 0.114 0.669 0.011 160 Majors form SP Joe Blanton 32 0.748 0.053 0.786 0.037 168 2007 outlier CL Jonathan Papelbon 32 0.572 0.000 0.621 0.049 65 In form 0.767 441 0.688 1458

PART II

BASEBALL WIN ACCOUNTING

1. Introduction and Overview

Baseball generates a lot of statistics. In the age of computers, the traditional stats have been supplemented by a wide range of new and often complex ones. But the proliferation of statistics is not necessarily adding all that much to our understanding of the game. Indeed, a good case can be made that a lot of the statistics – especially the situational ones – and the attention given to some particular statistics because of Fantasy Baseball may even be detracting from promoting a better understanding of the game.

This book doesn’t introduce any new statistics. Rather, it shows how to use powerful existing statistics, ones with which we’re already familiar, to better advantage. Baseball Win Accounting is an innovative way to allow us to make judgments about the game that we are making intuitively on a daily basis, but in a more precise fashion:  How much of a team’s success can be attributed to individual players or pitchers?  How can we measure the impact of trading a pitcher for a hitter?  What is the contribution to a team's wins of good fielding? Is the contribution to wins of good defense at shortstop the same as at second base?  How much are teams actually paying for additional wins with the astronomical contracts being given to star players today? These are not questions that can be rigorously analyzed using the performance measures such as batting averages, ERAs or fielding averages because these do not allow a direct comparison of hitting versus pitching versus fielding and they do not translate individual player/pitcher performance to team accomplishments. Nor can such questions be analyzed by giving teams subjective “power ratings” or giving players numerical grades (for example, 1-10 or A, B, C or D or some other scale) as some analysts do. Remarkably, many common statistics such as pitcher's "wins" attribute to individuals what is a team outcome. Even more remarkably, many sophisticated analytical methods, for example Bill James' calculation of individual player "winning percentages", also attribute to individuals what only a team can do. These confusions beg to be cleared up.

Baseball Win Accounting explains team accomplishments in terms of individual player performances and does not confuse the two. In team sports, teams win and lose games, not individuals.

To get at these questions in a rigorous way, the contributions of hitters, pitchers and fielders have to be expressed in a common statistic that is both a meaningful measure of individual performance and also, at the team level, closely correlated with team success. This can be done by applying analytically the statistical information already at our disposal – this is what the Baseball Win Accounting framework does.

The key to any accounting framework is to use a common basis for evaluation. Of the readily available statistics that are the same for both pitchers and hitters, by far the most meaningful is the sum of the on-base percentage and slugging average (OPS) for hitters and the corresponding on-base plus slugging percentage against (OPSA) for pitchers. There are good reasons for choosing OPS/OPSA over other available options.  OPS is known and it is simple – you do not need a math degree or a computer to do the calculations in your head. This is important because we are not doing rocket science here. What we want is to give the average fan a rigorous but simple way to tackle hard questions.  OPS is available for both hitters and pitchers; moreover, fielding can also be expressed in terms of reduction of the opposition OPS, allowing this dimension of baseball to be integrated.  It allows us to go from player performance to team performance because: (a) a team’s OPS and OPSA are the sums of the individual OPSs/OPSAs of hitters and pitchers respectively (weighted by plate appearances and innings pitched respectively); and (b) a team's OPS and OPSA are highly correlated with the team's runs for and against. Since a team’s record on runs for and against is in turn highly correlated with team wins, this allows us to go from individual player performance to team performance.  There is no direct interdependence between the OPS performance of any two individual players. There is no direct interdependence between the OPSAs of any two pitchers. This means that we can add up individual player/pitcher OPS/OPSAs without counting. There is of course no way to separate the contributions of pitchers and fielders to making outs since the pitchers' OPSAs reflect fully the contribution of the fielders – this does not, however, prevent the use of this method to assess the marginal impact of changing a fielder. So what is the Baseball Win Accounting framework? The box below summarizes the elements; the rest of this book develops the underpinning for each of the relationships.

6. Team OPS = the average of the individual players’ OPSs, weighted by plate appearances. 7. Team OPSA = the average of the individual pitchers’ OPSAs, weighted by innings pitched. 8. One additional point on a team’s OPS translates into two additional runs scored over the course of a 162 game season. 9. One additional point on a team’s OPSA translates into two additional runs scored against over the course of a 162 game season. 10. When baseball is in a zone where games average nine runs per game, adding nine additional runs to a team’s margin of runs for and against over the course of a season adds on average one more win to the team’s total.

These relationships are not iron-clad laws of nature; but they are robust. Obviously, this method for win accounting cannot – and does not attempt to – explain everything about baseball outcomes. It does however put our thinking about the relationship between player performance and team outcomes into the right ballpark, so to speak. Let’s walk through an example:

Mark McGwire’s Win Contribution to the St. Louis Cardinals in 1998: Here’s how Baseball Win Accounting analyzes Mark McGwire’s contribution to the St. Louis Cardinals team performance in 1998 when he 70 home runs. A first and obvious point is that what counts is McGwire’s performance relative to the league. As shown in the panel below, Mark McGwire’s OPS in 1998 was .481 higher than an average NL hitter’s.

On-base Percentage Slugging Percentage OPS Mark McGwire .470 .752 1.222 Average .331 .410 .741 Difference .139 .342 .481

As shown in the panel below, McGwire had 681 total plate appearances, which accounted for 10.6% of the Cardinals’ team total of 6,413.1 That is typical. Most regulars get about 10-11% of a team’s plate appearances.

AB BB HB Sac Total Plate Appearances Mark McGwire 509 162 6 4 681 St Louis Cardinals 5,593 676 42 102 6,413 McGwire's share 9.10 23.96 14.29 3.92 10.62

The Baseball Win Accounting relationship then goes as follows (adopting for convenience, financial market practice and calling each point of OPS one basis point):

 McGwire’s OPS was 481 basis points higher than an average NL hitter’s; with 10.6% of the Cardinals’ plate appearances he boosted the team OPS by 51 basis points (10.6% of 481) compared to an average hitter.  Baseball Win Accounting asserts that each additional basis point was worth 2 additional runs for St. Louis over the course of the season. McGwire thus boosted St. Louis’ team run total by 102 runs compared to an average hitter.  Baseball Win Accounting says that each additional 9 runs for St. Louis meant one additional win. The 102 additional runs thus mean that St. Louis would be strengthened sufficiently to win 11 more games (102 divided by 9) than they would with an average hitter taking McGwire’s plate appearances.2

1 In 2000, the average AL team had 6,389 plate appearances while the average NL team had 6,375. In this book, I round up to 6,400 as the "norm" for a major league season. 2 As discussed later in this book, this particular rule is a linear approximation of Bill James’ well-known “Pythagorean Theorem”. If the latter rule were used to calculate the contribution of Mark McGwire to St. Louis in terms of extra wins, based on the 102 additional runs, the result would be 10 additional wins. As will be discussed later, the difference in the two rules derives from a basic non-linearity in the underlying relationship. In recent years when the average number of runs scored in major league ball has been close to 10 per game, the best “fit” between run margin and wins emerges using 10 as the number of additional runs for an extra team win. If you use the rule of 10 instead of 9, the number of additional wins that St. Louis would be estimated to have with McGuire compared to an average player would agree with the Pythagorean Theorem results – 10 instead of 11. Quite frankly, this degree of precision is not materially significant. For most players the answer will be identical when rounded to the nearest win. The -off for a slightly better fit is greater complexity and we want to “keep it simple”. Of course, one could calculate the gain compared to a "replacement" player level. In terms of batting average, a hitter generally cannot stay in the lineup if he falls below .200 – the so-dubbed "Mendoza Line" (after the unfortunate Minnie Mendoza) – because it is generally possible to bring up a minor leaguer and obtain that level of performance. For OPS, the Mendoza line is .600. Compared to a replacement level, McGwire's 1998 performance generated 132 more runs or 15 more wins. This is really significant: this is the difference between an 81-win season and a 96-win season.

An equally interesting question is how would the Cards have done with, say, other All-Star caliber players such as , Andres Galarraga or at first base in 1998 instead of McGuire? The answer is simple: McGwire boosted team wins by an impressive 5 to 6 games compared to the All-Star performances of these other top NL first basemen that year.

1998 OPS Drop-off from Drop-off in Team Drop-off in Drop-off in McGuire’s OPS OPS expressed in Team Runs Team Wins of 1.222 Basis Points Scored John Olerud .998 .224 24 48 5 Andres Galarraga .992 .230 24 48 5 Jeff Bagwell .981 .241 26 52 6

Pedro Martinez' Win Contribution to the in 2000: Here’s how Baseball Win Accounting analyzes Pedro Martinez' contribution to the Bosox in his 2000 season. Martinez’ OPSA in 2000 was .472 compared to the average of .792. Martinez therefore had an OPSA that was 320 basis points lower than an average AL pitcher.

On-base Percentage Slugging Percentage OPSA Against Against Pedro Martinez .213 .259 .472 American League Average .349 .443 .792 Difference -.136 -.184 -.320

Martinez pitched 217 innings, which accounted for 14.94% of the Red Sox team total of 1,453. That is typical for a member of a starting rotation. A good will typically account for about 15% of a team’s innings pitched, barring injury.3

Total Innings Pitched Pedro Martinez 217 Boston Red Sox 1,453 Martinez' share 14.94%

3 Team innings pitched vary quite a bit due to extra- games, not pitching the ninth inning in games lost on the road, games shortened due to rain, and sometimes playing a game more or less than the 162 scheduled. In 2000, the pitched the fewest innings with only 1,424.1 while the Brewers led the majors with 1,466.1 innings pitched. The AL average was 1,439; the NL average was 1,444. In a stylized 162 game season of 9 innings pitched per game, there would be 1,458 innings pitched. In this book, I round this to 1,460 innings pitched as the standard. Starting pitchers with between 200 and 240 innings pitched account for about 14 to 16% of team innings pitched, so 15% is a good round figure approximation for any starter. Baseball Win Accounting calculates Martinez' contribution to the Boston Red Sox in 2000 as follows:

 Martinez' OPSA was 320 basis points lower than an average AL pitcher's; with 14.94% of the Red Sox innings pitched he lowered the Red Sox team OPSA by 47.8 basis points (14.94% of 320) compared to an average pitcher.  Baseball Win Accounting asserts that each basis point by which Boston's OPSA was reduced lowered runs against Boston by 2 over the course of the season. Martinez thus lowered Boston's runs against total by 96 runs compared to an average pitcher.  Baseball Win Accounting says that each reduction of 9 runs for Boston meant one additional win. The 96 fewer runs thus meant about 11 more wins (96 divided by 9) for the Red Sox than they would have had with an average pitcher taking Martinez' innings.4  Compared to a replacement level pitcher (taking .900 as the "Mendoza Line" for OPSA), Pedro's 2000 season added 14 wins to Boston's total. Compared to ' 2000 season, the second-best in ERA terms in the AL in 2000, Pedro added a remarkable 7 more wins to Boston's total.

Which team would win out from trade of McGwire for Martinez? On the one-season basis, it would have been about even – the difference between the two was really no more than rounding error.5 What is important here is that this simple approach provides a direct way to compare hitter and pitcher contributions to team wins. This is how an MVP award can be judged in meaningful quantitative terms.

Can defense be brought into the same framework as hitting and pitching? Any baseball highlight reel is full of great defensive plays – an diving into the hole and bouncing up to gun out a hitter at first or an climbing the wall to pull back a potential homer. Yet, this recognition does not carry over into the statistical realm. In fact, one of the most difficult things to do working with the current set of baseball statistics is to integrate evaluations of defensive ability into team assessments and to identify separately the contribution of defense to team wins from the contribution of the pitchers.

Can defense be brought into the framework? While somewhat more difficult, indeed it can. Fielders turn balls that are put in play into outs. Their efficiency at doing this, relative to the league average for their position, defines their contribution to improving or worsening a team’s OPSA relative to the league average. This statistic is the defense efficiency ratio (DER).

It is not enough, however, to know a player’s DER. We know, for example, that centre fielders cut off doubles and triples by getting to balls hit in the gap and sometimes catch balls about to go

4 Using the Pythagorean theorem, Martinez generated 10 additional wins for Boston by reducing the number of runs against by 96. 5 Indeed, because McGwire played in a higher run environment than Martinez, his contribution (9.51 additional wins) comes out slightly less than Martinez’ (9.81 additional wins) when using the Pythagorean Theorem. In round numbers, however, these seasons were equivalent, and this is so using either the Pythagorean Theorem or the linear approximation. over the fence for a . by contrast prevent mostly singles. This is an important difference. As well, preventing a hit (or avoiding an error which is more or less the same thing) also avoids an extra against. All of this can be quantified. As is shown later in this book, the gain in wins from having a top defensive fielder at the positions where defense counts most (center field and shortstop) versus having an average defensive player is at most 3 wins. In 2001, the Tampa Bay Devil Rays decided to go with slick-fielding shortstop Felix Martinez in good measure because the team management associated a steep decline in the team's ERA in the second half of 2000 with his insertion into the starting lineup. Baseball Win Accounting can show whether this makes sense or mistakenly attributes an improvement in the pitching staff to the fielding prowess of Martinez.

When it comes to , managers place great emphasis on the ability to handle the pitching staff and will keep a good staff-handler in the lineup despite weak hitting. of the 2001 St. Louis Cardinals, for example, has such a reputation. Insofar as a 's talents mean anything, they will be reflected in a reduced OPSA for the staff when he is behind the plate versus another catcher. Baseball Win Accounting can translate this into team wins and provide a way to answer the question: who should get the majority of starts, the good hitting catcher or the guy who handles the staff better?

In short, Baseball Win Accounting provides a basis for comparison of the contributions of a Mark McGwire, a Pedro Martinez, a Felix Martinez or a Mike Matheny. And by the same token, it allows us to evaluate the impact of a trade of a pitcher for a hitter, of a good hitter for a good fielder and so forth. And isn't that something?

Were the Yankees that good in 1998 when they won 114 games, or were they lucky? The Yankees had a season for the ages in 1998, raising the inevitable question of how they ranked compared to other great teams. However, it was a bit of a mystifying season as well because this was a team that did not seem anywhere near as stacked with great players as their record suggested. This led to much discussion about the role of team play, the clubhouse, ’s contributions etc. This is the sort of issue on which light can be shed by an accounting framework such as provided by Baseball Win Accounting. Below is a table with the projection I did for the New York Yankees at the beginning of the 1998 season, along with the actual outcomes.

New York Yankees 1998 Actual 1998 Predicted 1997 1996 1995 Ave 95-97

Team OPS .824 0.800 0.798 0.796 0.777 0.790 Team OPSA .699 0.731 0.716 0.745 0.754 0.738 Estimated Runs 919 871 867 863 825 852 Actual Runs 965 891 871 837 866 Estimated Runs Against 669 733 703 761 779 748 Actual Runs Against 656 688 787 769 748 Estimated Margin 250 138 164 102 46 104 Actual Margin 309 203 84 68 118 Estimated Wins 1 109 96 99 92 86 92 Estimated Wins 2 115 104 90 89 94 Actual Wins 114 96 92 88 92 * Note: Estimated Wins 1 is based on Estimated Margin; Estimated Wins 2 is based on Actual Margins

The Yankees looked strong going into the 1998 season, but not as good as 114 wins. Quite a few forecasters picked the Yankees to finish first in the AL East. However, they were only expected to win in the low 90s (Sports Forecaster was the highest of the published forecasts that I found at 94). What happened? 1. The Yankees team OPS came in at .824 rather than .800 as I projected. Their team OPSA came in at .699 versus the .731 projected. The margin of OPS-OPSA thus came in at 125 basis points rather than the 69 projected. 2. On top of that, the Yankees scored 965 runs versus the 919 that OPS of .824 would predict. And they gave up 656 runs compared to the 669 that their OPSA would predict. As a result their actual margin of runs for and against was 309 rather than the 250 that their OPS and OPSA stats predict. 3. And they converted the 309 runs into 114 wins versus the 115 that an actual margin of 309 would predict.

The Yankees just had one of those seasons where an awful lot of things worked out just right. Except for converting run margin into wins, the Yankees did better than they should have. There were no individual performances that really stood out compared to previous career performances (except perhaps Scott Brosius in his big comeback year and Shane Spencer in his phenomenal 73 plate appearances); however, a lot of hitters did a bit better than their career records would have predicted. By the same token, not every pitcher had a great season. Some Yankee starters did better than expected but others did less well than projected. Only one really unexpected pitcher emerged (Orlando Hernandez). For the most part, the individual variations were actually well within the range of normal fluctuation from year to year. On balance, however, the good years were stronger on the up-side than the off-years on the down-side.

Below is a summary of the Yankee’s individual player performances in 1998, as I projected them at the start of the season and as they turned out. Players who did not figure in my projections but got playing time are presented in italics.

Yankees Player Performances in 1998: Actual versus Projected Projected Actual Difference Players PA98 OPS98 PA98 OPS98 Knoblauch Chuck 700 0.810 706 .766 -.044 Jeter Derek 700 0.800 694 .865 .065 Williams Bernie 650 0.900 578 .997 .097 Martinez Tino 650 0.875 608 .860 -.015 O'Neill Paul 600 0.875 672 .882 .007 Davis Chili 550 0.825 118 .820 -.005 Strawberry Darryl 400 0.800 345 .896 .096 Brosius Scott 550 0.700 603 .843 .143 Girardi Joe 350 0.650 279 .703 .053 Posada Jorge 300 0.775 409 .825 .050 Sojo Luis 100 0.650 153 .515 -.135 Bush Homer 100 0.600 78 .886 .286 Sveum Dale 200 0.675 64 .358 -.317 Curtis Chad 250 0.750 545 .715 -.035 Raines Tim 300 0.825 382 .778 -.047 Shane Spencer - - 73 1.321 Ricky Ledee - - 87 .691 Pitchers IP98 OPS98 IP98 OPS98 Difference Pettite Andy 240 0.700 216 .739 .039 Cone David 200 0.650 208 .673 .023 Wells David 220 0.750 214 .663 -.087 Irabu Hideki 180 0.800 173 .726 -.074 Mendoza Ramiro 160 0.825 130 .693 -.132 Rivera Mariano 70 0.625 61 .579 -.046 Nelson Jeff 80 0.650 40 .779 .129 Stanton Mike 60 0.675 79 .721 .046 Lloyd Graeme 50 0.750 38 .528 -.222 Holmes Darren 60 0.775 51 .714 -.061 Banks Willie 100 0.750 14 1.021 .271 Borowski Joe 40 0.825 10 .725 -.100 Hernandez Orlando - - 141 .640 Buddie Mike - - 42 .789 Others 39 .810

Finally, while the Yankees’ actual outcomes in terms of team runs for and against were a bit better than they had a right to expect, they were not better by a huge amount – indeed quite a few other teams also “over-achieved” in this sense and by as much as the Yankees. So what lessons do we draw from this case? 1. As Yogi Berra once said, forecasting is hard, especially when it’s about the future. 2. It proves that it is not unreasonable for hope to spring eternal in – the way the Yankees exceeded expectations is not out of reach of other teams. Like the kid in the movie Angels in the Outfield said: “It can happen”. The Yankees had a 18-game swing on expectations without any individual player doing anything extraordinary compared to his history and with no real windfall of pleasant minor league surprises. Such a swing can take a 70-win team to 90 and into the playoffs. It can be done and fans should not stop dreaming impossible dreams. 3. However, this kind of variation can work against a team as well – for example, it is equally possible for things to add up negatively for a team. Hasty decisions to break up teams that fail to live up to expectations in a half-season should accordingly be avoided. In baseball, you have to be patient! Especially in this day of outrageous salaries, general managers who cut and run and disband teams in mid-season may be destroying a lot of teams before their time. 4. Finally, a good case can be made on the basis of the above that the Yankees were full value for 109 wins and basically lucky to win the extra 5.

Were the Yankees the best team in baseball in 1998? It is perfectly fair to say that the Yankees had the best season that year; whether they were the best team is debatable. The Atlanta Braves had an OPS of .795, which was 131 basis points higher than their OPSA of .656. In Baseball Win Accounting terms, the Braves were good for 29 wins over .500 or 110 wins. The Braves actually won 106, or 4 fewer than the quality of their individual player performances warranted. What is of interest here is that the Braves "deserved" 1 more win than the Yankees "deserved". If we could replay the 1998 season as a laboratory experiment, with the players performing at their same levels but leaving the individual game outcomes to be determined as luck would have them, I think that over the long run, the 1998 Braves would just edge out the 1998 Yankees in winning percentage.

* * *

The discussion above shows that Baseball Win Accounting allows us to get at questions such as who got the better of trades, who really was the MVP, how much teams are paying for extra wins when they hand out astronomical contracts, and how good teams really are. This would not end controversy about these issues; but it would help to calibrate the discussion and confront opinions with meaningful statistical comparisons.

But Baseball Win Accounting allows us to do much more. It provides a framework in which to explore – and in fact to measure – the random component in baseball: why, for example, is run margin a significant determinant of team wins over the course of a full season even though it is totally immaterial in a game whether a team wins by 1 run or by 15? As can be readily observed, top baseball teams have winning percentages in the .600s and the cellar dwellers in the .400s – these percentages are not all that much different from a perfectly random .500. By contrast, top hockey teams finish in the .700s, top basketball teams in the .800s and the best football teams are in the .900s (and who can forget the Miami Dolphins' historic 1.000?). To what extent is baseball random? How much of it is luck? Baseball Win Accounting provides a way to get at this sort of question.

Here’s another question: what is the better yardstick for grading teams – the regular season or the playoffs? The answer here is simple: any appreciation of the extent of the randomness in the outcome of individual baseball events leads one to conclude that the outcome of a single game or short series between roughly equivalent teams is a roll of the dice. Clearly, the regular season is the only meaningful measure of a team. The was originally sponsored as a post- season exhibition series. It should have remained that way. Playoffs simply mean nothing in baseball.

Baseball Win Accounting also prompts the exploration of questions such as why a summary statistic like OPS/OPSA does so well in explaining outcomes, when so much more goes into explaining the outcome of an individual game? For example, why do stolen bases wash out as an explanation of team runs over a season when we know that they can make the difference in a close game? As is argued in this book, there is something akin to an evolutionary arms race in baseball: the faster runners get, the more teams select catchers for arm strength and less for hitting – and train pitchers for pick-offs. In other words, teams intuitively make tradeoffs that drive the success ratio of stolen bases to about the point where they are neutral to the outcome of a season – that is, to the point where the benefits of successful steals in increasing runs scored is more or less exactly negated by the cost of being caught stealing in lowering run scored. This is why OPS/OPSA are effective at the team-season level even though you would not manage a game on that basis.

So there is a sampling of what lies in the next several chapters. I hope that you are intrigued to read the full account.